Social network posting analysis using degree of separation correlation

ABSTRACT

A degree of social network separation of a social network user that generated expressive content of a social media posting is identified relative to a specified social network user for each of a group of social media postings. Social media postings with an equivalent identified degree of social network separation relative to the specified social network user are grouped. Differences between the expressive content of the grouped social media postings at different degrees of social network separation are determined. The determined differences between the expressive content of the grouped social media postings at the different degrees of social network separation are rendered.

BACKGROUND

The present invention relates to social network posting analysis. Moreparticularly, the present invention relates to social network postinganalysis using degree-of-separation correlation.

Social networks allow people (termed “social network contacts” withinthe social networks) to interact within an online environment. Socialnetworks allow social network contacts to communicate with one anotherusing a common platform. Social network contacts may submit pictures andstories for other social network contacts to view and read.

BRIEF SUMMARY

A method includes identifying, via a processor, relative to a specifiedsocial network user for each of a plurality of social media postings, adegree of social network separation of a social network user thatgenerated expressive content of the respective social media posting;grouping social media postings that comprise an equivalent identifieddegree of social network separation relative to the specified socialnetwork user; determining differences between the expressive content ofthe grouped social media postings at different degrees of social networkseparation; and rendering the determined differences between theexpressive content of the grouped social media postings at the differentdegrees of social network separation.

A system includes an output device; and a processor programmed to:identify, relative to a specified social network user for each of aplurality of social media postings, a degree of social networkseparation of a social network user that generated expressive content ofthe respective social media posting; group social media postings thatcomprise an equivalent identified degree of social network separationrelative to the specified social network user; determine differencesbetween the expressive content of the grouped social media postings atdifferent degrees of social network separation; and render, via theoutput device, the determined differences between the expressive contentof the grouped social media postings at the different degrees of socialnetwork separation.

A computer program product includes a computer readable storage mediumhaving computer readable program code embodied therewith, where thecomputer readable program code when executed on a computer causes thecomputer to: identify, relative to a specified social network user foreach of a plurality of social media postings, a degree of social networkseparation of a social network user that generated expressive content ofthe respective social media posting; group social media postings thatcomprise an equivalent identified degree of social network separationrelative to the specified social network user; determine differencesbetween the expressive content of the grouped social media postings atdifferent degrees of social network separation; and render thedetermined differences between the expressive content of the groupedsocial media postings at the different degrees of social networkseparation.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

FIG. 1 is a block diagram of an example of an implementation of a systemfor social network posting analysis using degree-of-separationcorrelation according to an embodiment of the present subject matter;

FIG. 2 is a block diagram of an example of an implementation of a coreprocessing module capable of performing social network posting analysisusing degree-of-separation correlation according to an embodiment of thepresent subject matter;

FIG. 3 is a flow chart of an example of an implementation of a processfor social network posting analysis using degree-of-separationcorrelation according to an embodiment of the present subject matter;and

FIG. 4 is a flow chart of an example of an implementation of a processfor social network posting analysis using degree-of-separationcorrelation using correlations of expressed opinions to identify opiniontrends and a degree of social network separation at which to focusefforts to influence opinion change according to an embodiment of thepresent subject matter.

DETAILED DESCRIPTION

The examples set forth below represent the necessary information toenable those skilled in the art to practice the invention and illustratethe best mode of practicing the invention. Upon reading the followingdescription in light of the accompanying drawing figures, those skilledin the art will understand the concepts of the invention and willrecognize applications of these concepts not particularly addressedherein. It should be understood that these concepts and applicationsfall within the scope of the disclosure and the accompanying claims.

The subject matter described herein provides social network postinganalysis using degree-of-separation correlation. The present technologyallows a social network user to collect, analyze and correlateexpressive content of social network postings (e.g., opinions,information, etc.) of other social network users into correlatedgroupings based upon a degree of social network separation from thesocial network user. The correlated groupings of social network postingsallow the social network user to evaluate differences between expressedideas of social network postings of other users within their immediatesocial network relative to the ideas expressed at different degrees ofsocial network separation from the social network user in one or moresocial networks. A visual user interface allows the social network userthat initiated or that is reviewing the correlated social networkpostings to graphically interpret different perspectives, opinions, andfeedback, at the different degrees of social network separation.Rendered output of the visual user interface may include determinedpredominant opinions within the respective degrees of social networkseparation, opinion trends across the different degrees of socialnetwork separation, deviations from target configured expressedopinions, degrees of separation at which to focus efforts to improveopinion change within a social network community or across differentsocial network communities, and other information as appropriate for agiven implementation.

As such, the visual user interface shows groupings of correlated socialnetwork postings, and visually depicts variations and changes of contentof the social network postings based on the degree of separation fromthe initiating or reviewing social network user. The correlatedgroupings of social network postings may be generated from varioussocial media inputs, such as social media channels, social networks,social network-based communities, network-based topic discussions andforum platforms, and other forms of electronic information sharingchannels.

Using the technology described herein, a business and/or user mayidentify a predominant opinion/perception of users on a topic, withindifferent degrees of social network separation within both theirimmediate and extended social networks, to analyze and determine whetherthe content of the social network postings changes based on the degreeof separation from their immediate network. As such, the business oruser may gather data to determine and identify changes within thecontent of the social network postings as the degree of social networkseparation within one or more social networks gets closer or furtheraway.

For purposes of the present description, the phrase “social networkdistance” and the phrase “degree of social network separation” may beused interchangeably, and may refer to a social network contact distancebetween two respective social network contacts. As such, a given socialnetwork user is presumed to be located/situated at an initial degree ofsocial network separation of zero (0). Where two social network usersare connected as friends within a social network, their respectivesocial network distance may be considered one (1), such that theirdegree of social network separation is one (1). Similarly, where twosocial network users are not directly connected as friends within thesocial network, but share at least one friend in common within thesocial network (e.g., their friend's friends), these social networkusers may be considered to have a social network distance of two (2),such that their degree of social network separation is two (2). Tofurther the present example, where two social network users are notconnected as friends and also do not have any friends in common withinthe social network, but at least one friend/contact of each socialnetwork user has at least one other friend/contact in common within thesocial network (e.g., friends of your friends' friends), these socialnetwork users may be considered to have a social network distance ofthree (3), such that their degree of social network separation is three(3). The degree-of-separation analysis may continue in this manner forfurther degrees of separation to identify social network distances anddegrees of social network separation between further removed socialnetwork users.

To provide an example use-case, it is assumed that a social network userwants to make a decision about purchasing a new product, such as acomputing device or a software application. The user may initiate a pollwithin their social network to ask social network users (social networkcontacts) for their opinions on the product under consideration.Expertise in this product category may not actually appear within thesocial network until a third (3rd) or fourth (4th) degree of separation.The present technology will allow the user to dynamically convert and beprovided with a visual representation of theperspectives/opinions/feedback and other social network postings fromthe user's social network based upon the degree of separation between asocial network contact with the expertise and the user that generatedthe initial posting.

As another example use-case, a business may desire to expand itscustomer base and may make a determination as to whether to invest in anew feature for a product to enhance user experience for existingcustomers as well as attract new customers. The business may initiate aquestion or discussion topic on multiple social networks to determinehow the particular feature may be received by both its current customersand non-customers. For purposes of the present example, it is assumedthat current customers are at a social network degree of separation ofone (1). The business may post the question or discussion topic, and mayuse the present technology to evaluate responses based upon the degreeof social network separation of respondents. As such, where theevaluation provided by the present technology identifies that at adistant degree of social network separation (e.g., two (2) or three (3)degrees of social network separation), there is a particularly highnumber of social network users that are interested in the particularfeature and have expressed a positive opinion on the product if thefeature is added, the business may determine that adding the feature mayimprove customer-base growth, and may make a more-informed decisionregarding the investment in the new feature for the product.

Similar examples apply to persons running for elected office that desireto improve their perception among voters or entities advocating forcertain public or other policies. Further, medical/hospital or pandemicanalysis may benefit from use of the present technology. Many otherexample areas of use of the present technology are possible and all suchareas of use are considered within the scope of the present subjectmatter.

Further, while the present examples utilize social media inputsassociated with social networking environments for purposes ofillustration, the present technology may be applied to electronicinformation sharing channels. For example, the present technology may beapplied to email communications, text message communications, commentpostings to sections of websites, and any other form of electroniccommunication for which content/opinions may be aggregated andcorrelated based upon a degree of separation. Accordingly, any suchforms of electronic information sharing channels are considered withinthe scope of the present subject matter.

It should be noted that conception of the present subject matterresulted from recognition of certain limitations associated with socialnetwork posting perspective and opinion analysis by businesses. Forexample, it was observed that using conventional technologies,businesses do not have a way to view data and assemble metrics basedupon a degree of social network separation of social media postings bysocial network users, and that, instead, businesses are required toassume that all perspectives and/or opinions expressed within a socialmedia forum and/or responses to polls have equal weighting. It wasdetermined that social media may be used to identify links betweenindividuals that may otherwise not be visible or apparent to either theindividuals or to the businesses. It was further determined thatbusinesses that desire to improve their respective product and/orservice offerings, that desire to improve their respective customer-baseretention, and that desire to improve their respective customer-basegrowth may benefit from new technology that evaluates social mediapostings by social network users (e.g., perspectives and opinions postedonline), and that identifies deviations and variations of the socialmedia postings at various degrees of social network separation of thesocial network users that generate the respective social media postings.The present subject matter improves social media posting analysis byproviding for social network posting analysis using degree-of-separationcorrelation, as described above and in more detail below. As such,improved social media posting analysis may be obtained through use ofthe present technology.

The social network posting analysis using degree-of-separationcorrelation described herein may be performed in real time to allowprompt filtering and weighting of social network postings and analysisof changes of perspective/opinion among social network contacts basedupon relative social network distances between the social networkcontacts that make the respective social network postings. For purposesof the present description, real time shall include any time frame ofsufficiently short duration as to provide reasonable response time forinformation processing acceptable to a user of the subject matterdescribed. Additionally, the term “real time” shall include what iscommonly termed “near real time”-generally meaning any time frame ofsufficiently short duration as to provide reasonable response time foron-demand information processing acceptable to a user of the subjectmatter described (e.g., within a portion of a second or within a fewseconds). These terms, while difficult to precisely define are wellunderstood by those skilled in the art.

FIG. 1 is a block diagram of an example of an implementation of a system100 for social network posting analysis using degree-of-separationcorrelation. A computing device_(—)1 102 through a computing device_N104 communicate via a network 106 with several other devices. The otherdevices include a server_(—)1 108 through a server_M 110.

As will be described in more detail below in association with FIG. 2through FIG. 4, the computing device_(—)1 102 through the computingdevice_N 104 and the server_(—)1 108 through the server_M 110 may eachprovide automated social network posting analysis usingdegree-of-separation correlation. The automated social network postinganalysis using degree-of-separation correlation is based upon analysis(e.g., with filtering, weighting, and other forms of data analytics) ofsocial media postings relative to a degree of separation of individualsocial network users. The automated social network posting analysisusing degree-of-separation correlation provides visualization of resultsfor the user to enhance understanding of differences ofopinion/perspective of social network users as the degree of separationincreases relative to any given social network user. As such, thepresent technology may be implemented at a user computing device orserver device level. A variety of possibilities exist for implementationof the present subject matter, and all such possibilities are consideredwithin the scope of the present subject matter.

It should be noted that any of the respective computing devicesdescribed in association with FIG. 1 may be portable computing devices,either by a user's ability to move the respective computing devices todifferent locations, or by the respective computing device's associationwith a portable platform, such as a plane, train, automobile, or othermoving vehicle. It should also be noted that the respective computingdevices may be any computing devices capable of processing informationas described above and in more detail below. For example, the respectivecomputing devices may include devices such as a personal computer (e.g.,desktop, laptop, etc.) or a handheld device (e.g., cellular telephone,personal digital assistant (PDA), email device, music recording orplayback device, tablet computing device, e-book reading device, etc.),a web server, application server, or other data server device, or anyother device capable of processing information as described above and inmore detail below.

The network 106 may include any form of interconnection suitable for theintended purpose, including a private or public network such as anintranet or the Internet, respectively, direct inter-moduleinterconnection, dial-up, wireless, or any other interconnectionmechanism capable of interconnecting the respective devices.

FIG. 2 is a block diagram of an example of an implementation of a coreprocessing module 200 capable of performing social network postinganalysis using degree-of-separation correlation. The core processingmodule 200 may be associated with either the computing device_(—)1 102through the computing device_N 104 or with the server_(—)1 108 throughthe server_M 110, as appropriate for a given implementation. As such,the core processing module 200 is described generally herein, though itis understood that many variations on implementation of the componentswithin the core processing module 200 are possible and all suchvariations are within the scope of the present subject matter.

Further, the core processing module 200 may provide different andcomplementary processing of social network postings and degrees ofseparation of associated social network users that have postedexpressive content in association with each implementation. As such, forany of the examples below, it is understood that any aspect offunctionality described with respect to any one device that is describedin conjunction with another device (e.g., sends/sending, etc.) is to beunderstood to concurrently describe the functionality of the otherrespective device (e.g., receives/receiving, etc.).

A central processing unit (CPU) 202 provides computer instructionexecution, computation, and other capabilities within the coreprocessing module 200. A display 204 provides visual information to auser of the core processing module 200 and an input device 206 providesinput capabilities for the user.

The display 204 may include any display device, such as a cathode raytube (CRT), liquid crystal display (LCD), light emitting diode (LED),electronic ink displays, projection, touchscreen, or other displayelement or panel. The input device 206 may include a computer keyboard,a keypad, a mouse, a pen, a joystick, touchscreen, or any other type ofinput device by which the user may interact with and respond toinformation on the display 204.

A communication module 208 provides interconnection capabilities thatallow the core processing module 200 to communicate with other moduleswithin the system 100. The communication module 208 may include anyelectrical, protocol, and protocol conversion capabilities useable toprovide interconnection capabilities, appropriate for a givenimplementation.

A memory 210 includes a social network posting content and analysisstorage and processing area 212 that stores content of social networkpostings and social network user/contact degrees of separation relativeto one another for analysis and correlation by the core processingmodule 200. As will be described in more detail below, the informationstored within the social network posting content and analysis storageand processing area 212 is used to collect, parse, dynamically convert,and correlate expressive content of social network postings (e.g.,opinions, information, etc.) of social network users into correlatedgroupings based upon a degree of social network separation from one ormore social network users.

It is understood that the memory 210 may include any combination ofvolatile and non-volatile memory suitable for the intended purpose,distributed or localized as appropriate, and may include other memorysegments not illustrated within the present example for ease ofillustration purposes. For example, the memory 210 may include a codestorage area, an operating system storage area, a code execution area,and a data area without departure from the scope of the present subjectmatter.

A social network posting processing and correlation module 214 is alsoillustrated. The social network posting processing and correlationmodule 214 provides analytical processing capabilities for collecting,parsing, dynamically converting, and correlating expressive content ofsocial network postings of social network users into correlatedgroupings based upon a degree of social network separation from one ormore social network users for the core processing module 200, asdescribed above and in more detail below. The social network postingprocessing and correlation module 214 implements the automated socialnetwork posting analysis using degree-of-separation correlation of thecore processing module 200.

It should also be noted that the social network posting processing andcorrelation module 214 may form a portion of other circuitry describedwithout departure from the scope of the present subject matter. Further,the social network posting processing and correlation module 214 mayalternatively be implemented as an application stored within the memory210. In such an implementation, the social network posting processingand correlation module 214 may include instructions executed by the CPU202 for performing the functionality described herein. The CPU 202 mayexecute these instructions to provide the processing capabilitiesdescribed above and in more detail below for the core processing module200. The social network posting processing and correlation module 214may form a portion of an interrupt service routine (ISR), a portion ofan operating system, a portion of a browser application, or a portion ofa separate application without departure from the scope of the presentsubject matter.

The CPU 202, the display 204, the input device 206, the communicationmodule 208, the memory 210, and the social network posting processingand correlation module 214 are interconnected via an interconnection216. The interconnection 216 may include a system bus, a network, or anyother interconnection capable of providing the respective componentswith suitable interconnection for the respective purpose.

Though the different modules illustrated within FIG. 2 are illustratedas component-level modules for ease of illustration and descriptionpurposes, it should be noted that these modules may include anyhardware, programmed processor(s), and memory used to carry out thefunctions of the respective modules as described above and in moredetail below. For example, the modules may include additional controllercircuitry in the form of application specific integrated circuits(ASICs), processors, antennas, and/or discrete integrated circuits andcomponents for performing communication and electrical controlactivities associated with the respective modules. Additionally, themodules may include interrupt-level, stack-level, and application-levelmodules as appropriate. Furthermore, the modules may include any memorycomponents used for storage, execution, and data processing forperforming processing activities associated with the respective modules.The modules may also form a portion of other circuitry described or maybe combined without departure from the scope of the present subjectmatter.

Additionally, while the core processing module 200 is illustrated withand has certain components described, other modules and components maybe associated with the core processing module 200 without departure fromthe scope of the present subject matter. Additionally, it should benoted that, while the core processing module 200 is described as asingle device for ease of illustration purposes, the components withinthe core processing module 200 may be co-located or distributed andinterconnected via a network without departure from the scope of thepresent subject matter. For a distributed arrangement, the display 204and the input device 206 may be located at a point of sale device,kiosk, or other location, while the CPU 202 and memory 210 may belocated at a local or remote server. Many other possible arrangementsfor components of the core processing module 200 are possible and allare considered within the scope of the present subject matter.Accordingly, the core processing module 200 may take many forms and maybe associated with many platforms.

FIG. 3 through FIG. 4 described below represent example processes thatmay be executed by devices, such as the core processing module 200, toperform the automated social network posting analysis usingdegree-of-separation correlation associated with the present subjectmatter. Many other variations on the example processes are possible andall are considered within the scope of the present subject matter. Theexample processes may be performed by modules, such as the socialnetwork posting processing and correlation module 214 and/or executed bythe CPU 202, associated with such devices. It should be noted that timeout procedures and other error control procedures are not illustratedwithin the example processes described below for ease of illustrationpurposes. However, it is understood that all such procedures areconsidered to be within the scope of the present subject matter.Further, the described processes may be combined, sequences of theprocessing described may be changed, and additional processing may beadded or removed without departure from the scope of the present subjectmatter.

FIG. 3 is a flow chart of an example of an implementation of a process300 for social network posting analysis using degree-of-separationcorrelation. At block 302, the process 300 identifies, via a processor,relative to a specified social network user for each of a plurality ofsocial media postings, a degree of social network separation of a socialnetwork user that generated expressive content of the respective socialmedia posting. At block 304, the process 300 groups social mediapostings that comprise an equivalent identified degree of social networkseparation relative to the specified social network user. At block 306,the process 300 determines differences between the expressive content ofthe grouped social media postings at different degrees of social networkseparation. At block 308, the process 300 renders the determineddifferences between the expressive content of the grouped social mediapostings at the different degrees of social network separation.

FIG. 4 is a flow chart of an example of an implementation of a process400 for social network posting analysis using degree-of-separationcorrelation using correlations of expressed opinions to identify opiniontrends and a degree of social network separation at which to focusefforts to influence opinion change. At decision point 402, the process400 makes a determination as to whether a request to process socialmedia postings has been detected. The request to process social mediapostings may be detected in response to a user indication via a userinterface, and the request may include a designation of a particularspecified social network user (e.g., an individual or business entity)relative to which to process the social media postings for degree ofsocial network separation analysis. The request to process social mediapostings may also include a designation of one or more networks acrosswhich to collect and process social media postings.

At block 404, the process 400 collects social media postings foranalysis. The social media postings may be collected from one or moresocial networks as specified by the user for the social media postingprocessing. At block 406, the process 400 identifies, for each collectedsocial network posting and relative to the specified social network userrelative to which to process the social media postings for degree ofsocial network separation analysis, a degree of social networkseparation of the social network user that generated expressive contentof the respective social media posting. As described above, theprocessing may include identifying the degree of social networkseparation across multiple social networks of the user that generatedthe expressive content of each respective social media posting relativeto the specified social network user relative to which to process thesocial media postings for degree of social network separation analysis.

At block 408, the process 400 groups the social media postings thatinclude an equivalent identified degree of social network separationrelative to the specified social network user. At block 410, the process400 analyzes the text of the expressive content of the social mediapostings within each group of social media postings. For example, theprocess 400 may parse the social media postings, identify the expressivecontent of the respective postings, and then evaluate the identifiedexpressive content to determine expressed opinions or positionsrepresented within the expressive content. At block 412, the process 400correlates, within each group of social media postings, similarexpressed opinions of social media postings into expressed opinion sets.

At block 414, the process 400 categorizes the expressive content of therespective expressed opinion sets within each group of social mediapostings based upon the frequency of similar expressive content.Categorizing the expressive content may include forming the expressedopinion sets and associating a value (e.g., rating, ranking, etc.) withthe sets based upon the frequency of similar expressed content todifferentiate expressed opinions/content within the different expressedopinion sets. The frequency of similar expressive content may beidentified as a number of times a particular opinion or perspective isexpressed regarding a topic of social network discussion (e.g., a poll,discussion thread, etc.) relative to other opinions or perspectivesregarding the particular topic.

At block 416, the process 400 identifies a highest-frequency (e.g., mostoften) expressed content (e.g., most frequent opinion) among the variousexpressed opinion sets within each group of social media postings as apredominant expressed content of the social network users in each groupof social media postings. As such, the process 400 may determinedifferences between the correlated expressed opinion sets within eachgroup of social media postings, and may determine a predominant opinionamong the correlated expressed opinion sets within each group of socialmedia postings.

At block 418, the process 400 determines differences between thepredominant opinions across the groups of social media postings at thedifferent degrees of social network separation (e.g., “inter-group”differences between the predominant opinions expressed within the groupsof social media postings at the different degrees of social networkseparation). At block 420, the process 400 determines a trend of opinionchange across the different degrees of social network separation.

At block 422, the process 400 identifies, among the different degrees ofsocial network separation, a degree of separation at which to focusefforts to influence opinion change. The identification of the degree ofsocial network separation at which to focus efforts to influence opinionchange may be based, for example, upon a greatest deviation of opinionwithin the expressed content relative to a target configured expressedopinion, relative to the determined trend of opinion change, or otherfactor(s) as appropriate for a given implementation.

At block 424, the process 400 graphically renders the determineddifferences between the expressive content of the grouped social mediapostings at the different degrees of social network separation, theidentified degree of separation at which to focus efforts to influenceopinion change, and the determined trend of opinion change across thedifferent degrees of social network separation. The process 400 returnsto decision point 402 and iterates as described above.

As such, the process 400 obtains social media postings from a variety ofsocial networks, and identifies a degree of social network separationfrom a given user (e.g., social network contact or business) associatedwith each posting. The process 400 groups the social media postings bythe degree of social network separation, analyzes the text content ofeach social media posting, and correlates within each group similarexpressed content into expressed opinion sets. The process 400categorizes the expressed content within each expressed opinion setbased upon a frequency of opinion relative to other opinion sets in eachgroup. The process 400 identifies the highest-frequency content as apredominant opinion among the opinion sets in each group. The process400 determines differences between predominant opinions across thegroups of social media postings at the different degrees of socialnetwork separation and determines a trend of opinion change across thedifferent degrees of social network separation. The process 400 furtheridentifies a degree of social network separation at which to focusefforts to influence opinion change. The process 400 graphically rendersthe determined differences between the expressive content of the groupedsocial media postings at the different degrees of social networkseparation, the identified degree of separation at which to focusefforts to influence opinion change, and the determined trend of opinionchange across the different degrees of social network separation.Accordingly, the present technology allows a business or other user toanalyze large sets of expressed content in the form of opinions, pollresponses, or other expressed content, and to determine degrees ofseparation with effective influence (e.g., users like the product,public office or other campaign, etc.) and areas at which to focusefforts to influence opinion change (e.g., convert users of otherproducts to customers, increase votes for the candidate or issue, etc.).

As described above in association with FIG. 1 through FIG. 4, theexample systems and processes provide social network posting analysisusing degree-of-separation correlation. Many other variations andadditional activities associated with social network posting analysisusing degree-of-separation correlation are possible and all areconsidered within the scope of the present subject matter.

Those skilled in the art will recognize, upon consideration of the aboveteachings, that certain of the above examples are based upon use of aprogrammed processor, such as the CPU 202. However, the invention is notlimited to such example embodiments, since other embodiments could beimplemented using hardware component equivalents such as special purposehardware and/or dedicated processors. Similarly, general purposecomputers, microprocessor based computers, micro-controllers, opticalcomputers, analog computers, dedicated processors, application specificcircuits and/or dedicated hard wired logic may be used to constructalternative equivalent embodiments.

As will be appreciated by one skilled in the art, aspects of the presentinvention may be embodied as a system, method or computer programproduct. Accordingly, aspects of the present invention may take the formof an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present invention may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination of the foregoing. Morespecific examples (a non-exhaustive list) of the computer readablestorage medium would include the following: an electrical connectionhaving one or more wires, a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), a portablecompact disc read-only memory (CD-ROM), an optical storage device, amagnetic storage device, or any suitable combination of the foregoing.In the context of this document, a computer readable storage medium maybe any tangible medium that can contain, or store a program for use byor in connection with an instruction execution system, apparatus, ordevice.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, RF, etc., or any suitable combination ofthe foregoing.

Computer program code for carrying out operations for aspects of thepresent invention may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as JAVA™, Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Aspects of the present invention have been described with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in acomputer-readable storage medium that can direct a computer or otherprogrammable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer-readablestorage medium produce an article of manufacture including instructionswhich implement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

A data processing system suitable for storing and/or executing programcode will include at least one processor coupled directly or indirectlyto memory elements through a system bus. The memory elements can includelocal memory employed during actual execution of the program code, bulkstorage, and cache memories which provide temporary storage of at leastsome program code in order to reduce the number of times code must beretrieved from bulk storage during execution.

Input/output or I/O devices (including but not limited to keyboards,displays, pointing devices, etc.) can be coupled to the system eitherdirectly or through intervening I/O controllers.

Network adapters may also be coupled to the system to enable the dataprocessing system to become coupled to other data processing systems orremote printers or storage devices through intervening private or publicnetworks. Modems, cable modems and Ethernet cards are just a few of thecurrently available types of network adapters.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a,” “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the present invention has been presented for purposes ofillustration and description, but is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the invention. Theembodiment was chosen and described in order to best explain theprinciples of the invention and the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

What is claimed is:
 1. A system, comprising: a output device; and aprocessor programmed to: identify, relative to a specified socialnetwork user for each of a plurality of social media postings, a degreeof social network separation of a social network user that generatedexpressive content of the respective social media posting; group socialmedia postings that comprise an equivalent identified degree of socialnetwork separation relative to the specified social network user;determine differences between the expressive content of the groupedsocial media postings at different degrees of social network separation;and render, via the output device, the determined differences betweenthe expressive content of the grouped social media postings at thedifferent degrees of social network separation.
 2. The system of claim1, where, in being programmed to identify, relative to the specifiedsocial network user for each of the plurality of social media postings,the degree of social network separation of the social network user thatgenerated the expressive content of the respective social media posting,the processor is programmed to identify the degree of social networkseparation across multiple social networks.
 3. The system of claim 1,where the expressive content of the grouped social media postingscomprises expressed opinions within the social media postings related toa topic, and where, in being programmed to determine the differencesbetween the expressive content of the grouped social media postings atthe different degrees of social network separation, the processor isprogrammed to: correlate, within each group of social media postings,similar expressed opinions of social media postings into expressedopinion sets; and determine differences between the correlated expressedopinion sets within each group of social media postings.
 4. The systemof claim 3, where, in being programmed to determine the differencesbetween the expressive content of the grouped social media postings atthe different degrees of social network separation, the processor isfurther programmed to: determine a predominant opinion among thecorrelated expressed opinion sets within each group of social mediapostings; and determine differences between the predominant opinionsacross the groups of social media postings at the different degrees ofsocial network separation.
 5. The system of claim 1, where, in beingprogrammed to: determine the differences between the expressive contentof the grouped social media postings at the different degrees of socialnetwork separation, the processor is programmed to determine a trend ofopinion change across the different degrees of social networkseparation; and render, via the output device, the determineddifferences between the expressive content of the grouped social mediapostings at the different degrees of social network separation, theprocessor is programmed to graphically render, via the output device,the determined trend of opinion change across the different degrees ofsocial network separation.
 6. The system of claim 1, where, in beingprogrammed to determine the differences between the expressive contentof the grouped social media postings at the different degrees of socialnetwork separation, the processor is programmed to: analyze text of theexpressive content of the social media postings within each group ofsocial media postings; categorize the expressive content based upon afrequency of similar expressive content within each group of socialmedia postings; and identify a highest-frequency expressive contentwithin each group of social media postings as a predominant expressivecontent of social network users in each group of social media postings.7. The system of claim 1, where the processor is further programmed to:identify, among the different degrees of social network separation, adegree of separation at which to focus efforts to influence opinionchange based upon a greatest deviation of opinion within the expressivecontent relative to a target configured opinion; and where, in beingprogrammed to render, via the output device, the determined differencesbetween the expressive content of the grouped social media postings atthe different degrees of social network separation, the processor isprogrammed to graphically render, via the output device, the identifieddegree of separation at which to focus the efforts to influence opinionchange.
 8. A computer program product, comprising: a non-transitorycomputer readable storage medium having computer readable program codeembodied therewith, where the computer readable program code whenexecuted on a computer causes the computer to: identify, relative to aspecified social network user for each of a plurality of social mediapostings, a degree of social network separation of a social network userthat generated expressive content of the respective social mediaposting; group social media postings that comprise an equivalentidentified degree of social network separation relative to the specifiedsocial network user; determine differences between the expressivecontent of the grouped social media postings at different degrees ofsocial network separation; and render the determined differences betweenthe expressive content of the grouped social media postings at thedifferent degrees of social network separation.
 9. The computer programproduct of claim 8, where, in causing the computer to identify relativeto the specified social network user for each of the plurality of socialmedia postings, the degree of social network separation of the socialnetwork user that generated the expressive content of the respectivesocial media posting, the computer readable program code when executedon the computer causes the computer to identify the degree of socialnetwork separation across multiple social networks.
 10. The computerprogram product of claim 8, where the expressive content of the groupedsocial media postings comprises expressed opinions within the socialmedia postings related to a topic, and where, in causing the computer todetermine the differences between the expressive content of the groupedsocial media postings at the different degrees of social networkseparation, the computer readable program code when executed on thecomputer causes the computer to: correlate, within each group of socialmedia postings, similar expressed opinions of social media postings intoexpressed opinion sets; and determine differences between the correlatedexpressed opinion sets within each group of social media postings. 11.The computer program product of claim 10, where, in causing the computerto determine the differences between the expressive content of thegrouped social media postings at the different degrees of social networkseparation, the computer readable program code when executed on thecomputer further causes the computer to: determine a predominant opinionamong the correlated expressed opinion sets within each group of socialmedia postings; and determine differences between the predominantopinions across the groups of social media postings at the differentdegrees of social network separation.
 12. The computer program productof claim 8, where, in causing the computer to: determine the differencesbetween the expressive content of the grouped social media postings atthe different degrees of social network separation, the computerreadable program code when executed on the computer causes the computerto determine a trend of opinion change across the different degrees ofsocial network separation; and render the determined differences betweenthe expressive content of the grouped social media postings at thedifferent degrees of social network separation, the computer readableprogram code when executed on the computer causes the computer tographically render the determined trend of opinion change across thedifferent degrees of social network separation.
 13. The computer programproduct of claim 8, where, in causing the computer to determine thedifferences between the expressive content of the grouped social mediapostings at the different degrees of social network separation, thecomputer readable program code when executed on the computer causes thecomputer to: analyze text of the expressive content of the social mediapostings within each group of social media postings; categorize theexpressive content based upon a frequency of similar expressive contentwithin each group of social media postings; and identify ahighest-frequency expressive content within each group of social mediapostings as a predominant expressive content of social network users ineach group of social media postings.
 14. The computer program product ofclaim 8, where the computer readable program code when executed on thecomputer further causes the computer to: identify, among the differentdegrees of social network separation, a degree of separation at which tofocus efforts to influence opinion change based upon a greatestdeviation of opinion within the expressive content relative to a targetconfigured opinion; and where, in causing the computer to render thedetermined differences between the expressive content of the groupedsocial media postings at the different degrees of social networkseparation, the computer readable program code when executed on thecomputer causes the computer to graphically render the identified degreeof separation at which to focus the efforts to influence opinion change.